A METHOD AND AN APPARATUS FOR UPDATING A DEEP NEURAL NETWORK-BASED IMAGE OR VIDEO DECODER

1 . A method, comprising:

obtaining a latent representative of at least one part of at least one image;

decoding at least one update parameter representative of a modification to apply to a deep neural network-based decoder;

modifying the deep neural network-based decoder based on the decoded update parameter, and

reconstructing the at least one part of at least one image from the latent using at least said modified deep neural network-based decoder.

2 . (canceled)

3 . (canceled)

4 . A method, comprising:

obtaining at least one update parameter for modifying a deep-neural-network-based decoder defined from a training of a deep neural network-based auto-encoder using a first training configuration, said at least one update parameter being obtained as a function of a training of said deep neural network-based auto-encoder using a second training configuration,

encoding at least one part of at least one image using at least the neural network-based auto-encoder trained using the second training configuration; and

encoding said at least one update parameter.

5 . (canceled)

6 . (canceled)

7 . An apparatus, comprising one or more processors, wherein said one or more processors are configured to:

obtain a latent representative of at least one part of at least one image;

decode at least one update parameter representative of a modification to apply to a deep neural network-based decoder;

modify a deep neural network-based decoder based on the decoded update parameter; and

reconstruct the at least one part of at least one image from the latent using at least said modified deep neural network-based decoder.

8 . (canceled)

9 . An apparatus, comprising one or more processors, wherein said one or more processors are configured to:

obtain at least one update parameter for modifying a deep-neural-network-based decoder defined from a training of a deep neural network-based auto-encoder using a first training configuration, said at least one update parameter being obtained as a function of a training of said deep neural network-based auto-encoder using a second training configuration;

encode at least one part of at least one image using at least the neural network-based auto-encoder trained using the second training configuration; and

encode said at least one update parameter.

10 . (canceled)

11 . The method of claim 1 , wherein modifying said deep neural network-based decoder comprises at least one of adding at least one new layer to said deep neural network-based decoder and updating at least one layer of a set of layers of said deep neural network-based decoder.

12 . (canceled)

13 . The method of claim 4 , wherein modifying said deep neural network-based decoder comprises at least one of adding at least one new layer to said deep neural network-based decoder and updating at least one layer of a set of layers of said deep neural network-based decoder.

14 . The method of claim 1 , wherein said deep neural network-based decoder comprises a hyper decoder configured for decoding side information used by an entropy decoder configured for entropy decoding said bitstream, and wherein modifying said deep neural network-based decoder comprises updating said hyper decoder.

15 . (canceled)

16 . The method of claim 1 , wherein said deep-neural-network-based decoder is configured for outputting first reconstructed data obtained with said deep-neural-network-based decoder, said first reconstructed data being used for reference by said deep-neural-network-based decoder, and wherein said deep-neural-network-based decoder is configured for outputting second reconstructed data obtained with said modified decoder, said second reconstructed data being used for display.

17 . The method of claim 4 , wherein obtaining said at least one update parameter comprises:

training said deep neural network-based auto-encoder using said first training configuration;

storing learnable parameters of a decoder of said deep neural network-based auto-encoder; and

retraining said deep-neural-network-based decoder using said second training configuration, wherein said retraining comprises modifying said deep neural network-based decoder, said at least one update parameter being representative of said modification.

18 . The method of claim 17 , wherein said retraining comprises jointly retraining an encoder part of said deep-neural-network-based auto-encoder using said second training configuration.

19 . (canceled)

20 . (canceled)

21 . The method of claim 4 , wherein said second training configuration comprises a loss function based on at least one of a subjective quality metric and a metric defined for a machine task or wherein said second training configuration comprises a dataset with specific video content type.

22 .- 27 . (canceled)

28 . The method of claim 1 , wherein said at least one update parameter comprises at least one of:

an indication of a number of layers to be updated of said deep-neural-network-based decoder;

an indication of whether a new layer is to be added to said deep-neural-network-based decoder;

an indication of whether a layer of said deep-neural-network-based decoder is updated by an increment of at least one weight of said layer;

an indication of whether a layer of said deep-neural-network-based decoder is updated by setting at least one new weight to said layer;

an indication of a position in a set of layers of said deep-neural-network based decoder of a layer to update of said deep-neural-network-based decoder;

an indication of a position in a set of layers of said deep-neural-network based decoder of a new layer to add;

an indication of a layer type of a layer to update or of a new layer;

an indication of a tensor dimension of a layer to update or of a new layer; and

at least one layer parameter of a layer to update or of a new layer.

29 .- 37 . (canceled)

38 . A computer readable medium comprising a bitstream comprising data representation of a latent representative of at least one part of at least one image and data representative of at least one update parameter representative of a modification to apply to a deep-neural-network-based decoder for reconstructing said at least one part of at least one image.

39 . A computer readable storage medium having stored thereon instructions for causing one or more processors to perform the method of claim 1 .

40 . A computer readable storage medium having stored thereon instructions for causing one or more processors to carry out the method of claim 4 .

41 . The computer readable medium of claim 38 , wherein said at least one update parameter comprises at least one of:

an indication of a number of layers to be updated of said deep-neural-network-based decoder;

an indication of whether a new layer is to be added to said deep-neural-network-based decoder;

an indication of whether a layer of said deep-neural-network-based decoder is updated by an increment of at least one weight of said layer;

an indication of whether a layer of said deep-neural-network-based decoder is updated by setting at least one new weight to said layer;

an indication of a position in a set of layers of said deep-neural-network based decoder of a layer to update of said deep-neural-network-based decoder;

an indication of a position in a set of layers of said deep-neural-network based decoder of a new layer to add;

an indication of a layer type of a layer to update or of a new layer;

an indication of a tensor dimension of a layer to update or of a new layer; and

at least one layer parameter of a layer to update or of a new layer.

42 . The method of claim 4 , wherein said at least one update parameter comprises at least one of:

an indication of a number of layers to be updated of said deep-neural-network-based decoder;

an indication of whether a new layer is to be added to said deep-neural-network-based decoder;

an indication of whether a layer of said deep-neural-network-based decoder is updated by an increment of at least one weight of said layer;

an indication of whether a layer of said deep-neural-network-based decoder is updated by setting at least one new weight to said layer;

an indication of a position in a set of layers of said deep-neural-network based decoder of a layer to update of said deep-neural-network-based decoder;

an indication of a position in a set of layers of said deep-neural-network based decoder of a new layer to add;

an indication of a layer type of a layer to update or of a new layer;

an indication of a tensor dimension of a layer to update or of a new layer; and

at least one layer parameter of a layer to update or of a new layer.

43 . The apparatus of claim 7 , wherein said at least one update parameter comprises at least one of:

an indication of a number of layers to be updated of said deep-neural-network-based decoder;

an indication of whether a new layer is to be added to said deep-neural-network-based decoder;

an indication of whether a layer of said deep-neural-network-based decoder is updated by an increment of at least one weight of said layer;

an indication of whether a layer of said deep-neural-network-based decoder is updated by setting at least one new weight to said layer;

an indication of a position in a set of layers of said deep-neural-network based decoder of a layer to update of said deep-neural-network-based decoder;

an indication of a position in a set of layers of said deep-neural-network based decoder of a new layer to add;

an indication of a layer type of a layer to update or of a new layer;

an indication of a tensor dimension of a layer to update or of a new layer; and

at least one layer parameter of a layer to update or of a new layer.

44 . The apparatus of claim 9 , wherein said at least one update parameter comprises at least one of:

an indication of a number of layers to be updated of said deep-neural-network-based decoder;

an indication of whether a new layer is to be added to said deep-neural-network-based decoder;

an indication of whether a layer of said deep-neural-network-based decoder is updated by an increment of at least one weight of said layer;

an indication of whether a layer of said deep-neural-network-based decoder is updated by setting at least one new weight to said layer;

an indication of a position in a set of layers of said deep-neural-network based decoder of a layer to update of said deep-neural-network-based decoder;

an indication of a position in a set of layers of said deep-neural-network based decoder of a new layer to add;

an indication of a layer type of a layer to update or of a new layer;

an indication of a tensor dimension of a layer to update or of a new layer; and

at least one layer parameter of a layer to update or of a new layer.

45 . The computer readable storage medium of claim 39 , wherein said at least one update parameter comprises at least one of:

an indication of a number of layers to be updated of said deep-neural-network-based decoder;

an indication of whether a new layer is to be added to said deep-neural-network-based decoder;

an indication of whether a layer of said deep-neural-network-based decoder is updated by an increment of at least one weight of said layer;

an indication of whether a layer of said deep-neural-network-based decoder is updated by setting at least one new weight to said layer;

an indication of a position in a set of layers of said deep-neural-network based decoder of a layer to update of said deep-neural-network-based decoder;

an indication of a position in a set of layers of said deep-neural-network based decoder of a new layer to add;

an indication of a layer type of a layer to update or of a new layer;

an indication of a tensor dimension of a layer to update or of a new layer; and

at least one layer parameter of a layer to update or of a new layer.